A project from the Social Media Research Foundation:

Transcription

1 A project from the Social Media Research Foundation:

2 About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group

3 Social Network Theory Central tenet Social structure emerges from the aggregate of relationships (ties) among members of a population Phenomena of interest Emergence of cliques and clusters from patterns of relationships Centrality (core), periphery (isolates), betweenness Methods Surveys, interviews, observations, log file analysis, computational analysis of matrices Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16 (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

4 SNA 101 B D F H A E I C G Node actor on which relationships act; 1-mode versus 2-mode networks Edge Relationship connecting nodes; can be directional Cohesive Sub-Group Well-connected group; clique; cluster Key Metrics Centrality (group or individual measure) Number of direct connections that individuals have with others in the group (usually look at incoming connections only) Measure at the individual node or group level Cohesion (group measure) Ease with which a network can connect Aggregate measure of shortest path between each node pair at network level reflects average distance Density (group measure) Robustness of the network Number of connections that exist in the group out of 100% possible Betweenness (individual measure) # shortest paths between each node pair that a node is on Measure at the individual node level Node roles Peripheral below average centrality Central connector above average centrality Broker above average betweenness A B D E C E D

5 Crowds matter

6 Kodak Brownie Snap- Shot Camera The first easy to use point and shoot!

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9 Social Media ( , Facebook, Twitter, YouTube, and more) is all about connections from people to people. 9

10 Patterns are left behind 10

11 There are many kinds of ties. Send, Mention, Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in

12 Think Link Nodes & Edges Is related to A Is related to B Is related to

13 World Wide Web Social media must contain one or more social networks

14 A network is born whenever two GUIDs are joined. Username Attributes Username Value, Value, value A B Vertex1 Vertex 2 Edge Attribute Vertex1 Attribute value value value

15 NodeXL imports edges from social media data sources

16 Location, Location, Location

17 Position, Position, Position

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19 Like MSPaint for graphs. the Community Mapping and Measuring Connections with

20 Now Available

21 Communities in Cyberspace

22 What we are trying to do: Open Tools, Open Data, Open Scholarship Build the Firefox of GraphML open tools for collecting and visualizing social media data Connect users to network analysis make network charts as easy as making a pie chart Connect researchers to social media data sources Archive: Be the Allen Very Large Telescope Array for Social Media data coordinate and aggregate the results of many user s data collection and analysis Create open access research papers & findings Make collections of connections easy for users to manage

23 Goal: Make SNA easier Existing Social Network Tools are challenging for many novice users Tools like Excel are widely used Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display

24 What we have done: Open Tools NodeXL Data providers ( spigots ) ThreadMill Message Board Exchange Enterprise Voson Hyperlink SharePoint Facebook Twitter YouTube Flickr

25 NodeXL Ribbon in Excel

26 What we have done: Open Data NodeXLGraphGallery.org User generated collection of network graphs, datasets and annotations Collective repository for the research community Published collections of data from a range of social media data sources to help students and researchers connect with data of interest and relevance

27 What we have done: Open Scholarship

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32 Example NodeXL data importer for Twitter

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41 Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.

42 Hubs

43 Bridges

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46 6 kinds of Twitter social media networks [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network

47 6 kinds of Twitter social media networks [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network

48 #My2K Polarized

49 #CMgrChat In-group / Community

50 Lumia Brand / Public Topic

51 #FLOTUS Bazaar

52 New York Times Article Paul Krugman Broadcast: Audience + Communities

53 Dell Listens/Dellcares Support

54 SNA questions for social media: 1. What does my topic network look like? 2. What does the topic I aspire to be look like? 3. What is the difference between #1 and #2? 4. How does my map change as I intervene? What does #YourHashtag look like?

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56 Top

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58 6 kinds of Twitter social media networks [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network

59 [Divided]Polarize d Crowds [Unified]Tig ht Crowd [Fragmented] Brand Clusters [Clustered] Communities [In-Hub & Spoke]Broadcast Network [Out-Hub & Spoke]Support Network [Low probability] Find bridge users. Encourage shared material. [Low probability] Get message out to disconnected communities. [Possible transition] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Remove bridges, highlight divisions. [Low probability] Get message out to disconnected communities. [High probability] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [High probability] Increase retention, build connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Undesirable transition] Increase population, reduce connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Low probability] Get message out to disconnected communities. [Possible transition] Increase retention, build connections. [High probability] Increase reply rate, reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Possible transition] Get message out to disconnected communities. [High probability] Increase retention, build connections. [High probability] Increase publication of new content and regularly create content.

60 C. Scott Dempwolf, PhD Research Assistant Professor & Director UMD - Morgan State Center for Economic Development

61 What is Social Network Analysis? How is it useful for the humanities? 1. New framework for analysis 2. Data visualization allows new perspectives less linear, more comprehensive Social Network Analysis and Ancient History Diane H. Cline, Ph.D. University of Cincinnati

62 Strategies for social media engagement based on social media network analysis

63 Request your own network map and report

64 What we want to do: (Build the tools to) map the social web Move NodeXL to the web: (Node[NOT]XL) Node for Google Doc Spreadsheets? WebGL Canvas? D3.JS? Sigma.JS Connect to more data sources of interest: RDF, MediaWikis, Gmail, NYT, Citation Networks Solve hard network manipulation UI problems: Modal transform, Time series, Automated layouts Grow and maintain archives of social media network data sets for research use. Improve network science education: Workshops on social media network analysis Live lectures and presentations Videos and training materials

65 How you can help Sponsor a feature Sponsor workshops Sponsor a student Schedule training Sponsor the foundation Donate your money, code, computation, storage, bandwidth, data or employee s time Help promote the work of the Social Media Research Foundation

66 Thank you!

67 A project from the Social Media Research Foundation:

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